Does the Human Gut Microbiome Serve as a Novel Personalized Therapeutic Target for Coronary Atherosclerosis?

Introduction:

The human gastrointestinal system is populated with a variety of symbiotic microorganisms, namely microbiota. Its total weight is approximately 2 kilograms, containing trillions of microorganisms. The microbiome is the total genetic (metagenomic) data of the microbiota. In recent years, the development of efficient methods for genome sequencing and bio-informatics has enabled fast and accurate quantification and qualification of the microbiome, made microbiome analysis leading method of microbiota research.

Coronary artery disease (CAD) accounted for more than 8 million deaths yearly worldwide. In particular, acute coronary syndrome (ACS) remains a major cause of morbidity and mortality and is responsible for more than 1 million hospital admissions in the United States annually. The pathophysiologic hallmark of ACS is coronary thrombosis caused by atherosclerotic plaque injury, with two types of injuries being described. The first is plaque rupture, which remains the most common cause of coronary athero-thrombosis, and the second is superficial plaque erosion which is recognized with increasing frequency. As opposed to plaque rupture, lesions that are caused by erosion do not have thin fibrous caps, abundant inflammatory cells, or a large lipid core, but rather rich in extracellular matrix, such as proteoglycans and glycosaminoglycans.

Imaging studies such as coronary computed tomographic angiography (CCTA) and diagnostic coronary catheterizations with or without optical coherence tomography (OCT) are being used increasingly in clinical practice in order to characterize the mechanism responsible for unstable/vulnerable atherosclerotic plaque.

The human gut microbiota interacts extensively with the host through metabolic exchange and co-metabolism of substrates; thereby contribute to a variety of metabolic and immunologic mechanism in the human body. CAD is a major field of interest in microbiota research, and there have been several findings that connect the gut microbiota to CAD pathophysiology. First, microbiota was associated with metabolic syndrome, namely obesity and insulin resistance. It is hypothesized that gut microbiota may increase short-chain fatty acid, that eventually increase appetite, thus causing obesity. Another hypothesis is that gut microbiota endotoxins may translocate into the bloodstream, elicit inflammatory cascade that eventually promote atherosclerosis. Second, microbiota may also have a role in the development of atherosclerosis. In patients with symptomatic atherosclerosis, there is a unique microbiome pattern that may have pro-inflammatory characteristics. Recently, a unique microbial pattern was found among patients with high cardiovascular risk profile. Third, gut microbiota metabolize dietary phosphatidylcholine (lecitine) to produce the metabolite trimethylamine-N-oxide (TMAO), which is associated with increased risk of cardiovascular events.

The data published so far relates solely to the interaction between human gut microbiome and cardiovascular risk factors. To the best of the investigator's knowledge and understanding, the microbiome analysis of patients with an established diagnosis of CAD (including ACS) is lacking.

Objectives:

The purpose of the current study is to investigate the gut microbiota of patients with symptomatic CAD, both in stable and during acute phases. The investigators hypothesize that the study participants would present with a unique microbiome signature that may provide a novel insight into the pathophysiology of atherosclerotic CAD while affording putative therapeutic implications.

After establishing the unique microbiome signature in a large cohort of CAD patients, the investigators would correlate it to TMAO levels to further investigate its part in the pathophysiology of CAD. In the final stage, the investigators would try to find ways to adjust personalized treatment options to change the "pro-atherosclerotic" gut microbiota. By using data from the current study with the investigators' previous 1000 patient cohort with known microbiome and nutritional profile, it would be able to search for a specific target for nutritional intervention, such as probiotics. Then, the investigators would monitor the patients with sequencing the microbiome after the nutritional intervention.

Methods:

Study design and recruitment. Study participants will be patients aged 30-80 arriving to Rabin Medical Center with suspected CAD and able to provide informed consent. Participants will provide medical, lifestyle, and nutritional questionnaires. Blood pressure and heart-rate measurements will be taken during hospitalization as well as blood tests and fecal samples and/or rectal swabs. In order to evaluate and/or treat suspected atherosclerotic disease participants will undergo cardiac CT and/or cardiac catheterization in accordance with the standard of care and based upon the decision of the treating cardiologist. Diagnostics and treatment options will be based only on participants' medical condition and regardless of the aforementioned study protocol.

The control group will be selected to represent an age, sex and cardiovascular risk factors -matched group without current CAD. Further exclusion criteria in the control group will be antibiotic consumption in the following 3 months, inflammatory bowel disease, or other significant chronic disease that may influence the microbiota (such as cancer, autoimmune disease, and chronic immunosuppressive treatment). The control group will undergo cardiac CT or coronary angiography according to clinical suspicion in order to rule-out CAD and irrespective of the study protocol.

Blood samples. 10 ml venous blood will be collected into Ethylenediaminetetraacetic acid (EDTA) and gel with clot activator -containing tubes from the enrolled patients in all study participants. The concentrations of serum creatinine, troponin, creatine phosphokinase (CPK), hemoglobin, triglycerides (TG), total cholesterol (TC), high-density lipoproteins (HDL), low-density lipoproteins (LDL), glucose, c-reactive protein (CRP), b-type natriuretic peptide (BNP) and hemoglobin A1c (HbA1C) will be measured by automatic biochemistry analyzer.

In addition, the levels of TMAO will be measured from blood plasma by using Ultra-High Performance Liquid Chromatography - Mass Spectrometric - Multiple Reaction Monitoring (UHPLC-MS/MRM) as previously described.

Nutritional profiling. All participants will report their habits of food consumption by filling up the Food Frequency Questionnaire (FFQ).

Cardiac CT analysis. Selected participants will undergo CT angiography for the evaluation and quantification of CAD using a 256-slice system (Brilliance iCT, Philips Healthcare, Cleveland, Ohio). Data will be acquired with a collimation of 96 X 0.625 mm and a gantry rotation time of 330 ms. Intravenous injection of 60 to 90 ml of nonionic contrast agent at a flow rate of 5 ml/s will be followed by a 30-ml saline chase bolus (3 ml/s). Acquisition will be performed during an inspiratory breath hold while the electrocardiogram will be recorded simultaneously to allow, dependent from the heart rate, retrospective or prospective gating of the data. All images will be reconstructed with a slice thickness of 0.67 mm and a slice increment of 0.34 mm. The complete dataset will be transmitted to a dedicated CT workstation with a 3-dimensional reconstruction tool specifically designed for coronary angiography (Philips Intellispace Portal, version 7.0) to allow for multiplanar reformations and quantitative plaque analysis. An independent reader will review all studies. Each vessel containing significant stenosis will be analyzed in curved multiplanar reformatted images in long-axis and cross-sectional views. Diameters at the site of maximum stenosis and at proximal and distal references will be measured. Degree of stenosis will be calculated as the ratio of the difference between the diameter at maximum stenosis and the mean of diameters at the proximal and distal references divided by the mean of diameters at proximal and distal references and expressed as percentage. Remodeling index will be calculated as the outer vessel area at the site of maximum stenosis divided by the mean of outer vessel areas at proximal and distal references. Positive remodeling will be defined as a remodeling index ≥ 1.05. Plaque volume will be automatically calculated as the volume of all voxels segmented between the luminal and outer vessel boundaries on curved multiplanar reformatted images. Proximal and distal references will be used as the proximal and distal ends of the plaques. The investigators will report the total volume of plaque and volumes of plaque subtypes: calcified, non-calcified, and mixed plaques.

Cardiac catheterization and percutaneous coronary intervention (PCI). Patients will be admitted to the catheterization laboratory according to their clinical presentation, taking into account the current ESC/AHA clinical guidelines. The cardiac catheterization procedure will be performed using standard percutaneous techniques via the radial or femoral artery. Coronary lesions will be evaluated by the operator in terms of stenosis by visual estimation or other using objective measurements, such as quantitative coronary analysis (QCA). Coronary intervention, including balloon angioplasty and stent implantation will be implemented as needed and according to the severity of coronary stenosis i.e. (≥70% diameter stenosis). Adjunctive coronary imaging (OCT or intravascular ultrasound) will be performed according to operator's discretion and regardless of the study protocol. All patients will be treated during the procedure with anticoagulation (mostly unfractionated heparin) using careful monitoring of activated clotting time between 250-300 seconds. After the angioplasty procedure, all patients will be treated with dual anti-platelet therapy combining aspirin and P2Y12 inhibitor (clopidogrel, prasugrel or ticagrelor, according to the clinical indication) for 6-12 months, unless there will be contra-indication, such as treatment with oral anti-coagulants.

Genomic DNA Extraction and Filtering. Genomic DNA from stool samples will be purified using PowerMag Soil DNA isolation kit (MoBio) optimized for Tecan automated platform. For shotgun sequencing, 100 ng of purified DNA will be sheared with a Covaris E220X sonicator.

Microbiome analysis. Microbiome samples will be processed by an automated robotic pipeline in 96-well format. Each sample group collected will be processed robotically for both 16S and metagenomic sequencing.

Generating microbiome-based features. The investigators will employ and further extend a computational pipeline that was developed for generating a rich set of features from a metagenomic sample. These features will be the basis for models that identify microbiome-based signatures.

Bacterial and viral abundances - Mapping the metagenome sample to a reference bacterial genome database, and then counting the number of reads mapping to each bacteria, resulting in a vector of relative bacterial abundances for each sample.

Bacterial diversity - Using the relative bacterial abundances derived above, the investigators will compute several measures of the diversity of bacteria and viruses in a metagenome sample (e.g., Shannon entropy of the relative abundance vector, number of bacteria above some minimal abundance level), as sample diversity was shown to be associated with certain physiological aspects of the host such as overall adiposity and insulin resistance.

Bacterial growth rates - For each metagenome sample, the investigators will compute a vector that corresponds to the growth rate of each bacteria in the sample, using a novel method that was recently developed for this purpose. Briefly, by examining the pattern of sequencing read coverage (depth) across the length of different bacterial genomes, the investigators found that many bacteria exhibit a prototypical coverage pattern, consisting of a single trough and a single peak. Notably, the location of the peak coincides with the bacteria's known origin of replication, suggesting that the added read coverage near the peak represents newly replicated DNA. For any given bacteria, the ratio between the peak and trough coverage varies greatly across samples from different human gut microbiomes, with high ratios being similar to those obtained during exponential growth phase of bacteria grown in culture, and low ratios being similar to growth in stationary phase.

Gene abundances - The investigators will compute the relative abundances of genes in metagenome samples by applying a similar approach to that above for deriving relative abundances of bacteria. To this end, rather than mapping the reads to the reference bacterial genome database, the investigators will map them to a reference database of bacterial genes which was recently extended and that collectively contain over 3 million distinct bacterial genes. The derived gene abundance vectors are complementary to the bacteria abundance vectors, with the advantage being mapping to genes whose embedding bacterial genome is unknown and thus allowing more metagenome sequencing reads to be mapped, and with the disadvantage of producing a much larger feature vector.

Biological pathway abundances - As another set of features that provide information at the functional level of the microbiota, the investigators will use the KEGG database of biological pathways51 and the above vector of gene abundance of each sample to compute an abundance score for each biological pathway. The key advantage of this set of features is that its associations provide direct hypotheses regarding the underlying mechanisms through which the microbiota may be involved with the correlated phenotype.

Sample storage. The samples as well as remaining DNA and frozen serums will be stored in -80C freezers. Unprocessed stool samples (e.g., not all samples collected annually will be processed initially) will also be stored in -800C freezers.

Statistical analysis. Clinical data such as vital signs, cardiovascular risk factors (age, gender, lipid profile, glycemic index, smoking status, previous CAD), chronic co-morbidities, regular drug use, imaging findings on cardiac CT and/or cardiac catheterization as well as cardiac enzymes levels will be collected at Rabin Medical Center. At the Weizmann institute of science, in the computer science department by the Segal Lab, the investigators will analyze the data. For each parameter an association analyses will be performed to identify all of the microbiome parameters that are associated with these clinical parameters.

After analyzing the microbiome signatures in those patients, where known probiotic and/or nutritional interventions can lead to the desired change the investigators will intervene and then monitor with sequencing the microbiome after the intervention.